code: def _select_names(est, in_names=None):
      mask = est.get_support(indices=False)
      in_names = _get_feature_names(
          est, feature_names=in_names, num_features=len(mask)
      )
      return [in_names[i] for i in np.flatnonzero(mask)]
  
file_path: eli5/eli5/sklearn/transform.py
imports: import numpy
  from sklearn.pipeline import Pipeline, FeatureUnion
  from sklearn.feature_selection.base import SelectorMixin
  from sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler, RobustScaler
  from eli5.transform import transform_feature_names
  from eli5.sklearn.utils import get_feature_names
label_desc: Use image node_id 1 for calling a global function with example usage: _select_names(est, in_names) and returns: unknown